Welcome to 2025! The good news is that you have a 12 month runway to hit your new targets. The bad news is that what worked for you in previous years will no longer be enough.
This year you’re competing against plants that are fully digitized and using powerful industrial AI tools that allow them to adapt faster to market challenges and bring high quality products to market –faster.
Supporting your team and operations with AI in 2025 is no longer a nice-to-have, it's a necessity.
The key decision for factories today is not whether they should use an industrial AI solution to increase productivity and production efficiencies, but how quickly they can test, deploy, and extract value from the right solution.
For sectors like steel, chemicals, cement, oil, and gas, becoming data-ready is the crucial first step towards all the automation, insight, and resource efficiencies provided by industrial AI. However, only one in five manufacturers consider themselves “data ready”.
Whether it’s outdated technology or outdated data collection practices that are holding you back, this article hopes to remove you from that statistic by providing the steps your company needs to know to become and maintain data readiness.
This article includes criteria for basic and advanced data readiness. Whether your goal is to accelerate a software pilot, or to become data-driven across your entire organization.
The Impact of Data Readiness
Data is the crucial fuel-source for any AI solution so the importance of data readiness in industrial sectors cannot be overstated. According to recent studies:
- 55% of companies have already adopted AI technologies, highlighting the growing trend towards AI-driven optimization.
- Organizations with AI-ready data can reduce their AI model development time by up to 70%, significantly accelerating their digital transformation efforts.
- Industrial companies that have successfully implemented AI report a 20-30% increase in overall equipment effectiveness (OEE) and vastly improved process and resource efficiencies.
- Factories using Fero Labs AI have increased their speed to solution by 90X. They’re fixing issues faster with AI-automated data preparation and powerful diagnostics to find the cause of any issue in minutes. Fero Labs users become less reactive and more proactive with greater focus on process optimization initiatives and outcomes without being constantly rerouted to solve new issues that occur.
Changing Old Behaviors
A recent study by the Manufacturing Leadership Council noted that 70% of manufacturers are still entering data manually. This is particularly prominent with process data, beyond that captured by IoT sensors, as the majority of senior process and quality engineers continue to track their operational activities manually on spreadsheets and paper.
The net impact of manual data collection is that critical production knowledge and company IP will walk out the door with these workers when they age-out for retirement.
This method also inhibits any training process for new hires. Engineer training is typically run across a lengthy ‘shadowing’ period where new hires are trained on the scenarios that occur during that period. When you're digitized, new hires can be trained on any scenario. Most importantly, this valuable IP will be available to train your AI/ML models as well.
Basic Data Readiness Essential Steps
1. Assess Your Current Data Landscape
Begin by evaluating the quality, security, and governance of your existing data. Identify areas for improvement and develop a plan to address them. This assessment should cover all data sources, including SCADA systems, PLCs, MES, ERP systems, and IoT sensors.
It’s important to note that different types of data will be valuable for different levels of activities. Data from ERP or MES can typically be used for production optimization, while IoT sensors can be used for predictive maintenance. Using an industrial AI solution that can ingest both will bring an additional layer of value to your business.
By example, combining your historian data with a built for purpose process optimization solution like Fero Labs, can go beyond predictive maintenance to uncover how process conditions can impact the lifecycle of your mechanical infrastructure. This asset maintenance enables your engineers to make process changes that will extend the life of equipment such as heat exchangers or catalysts, in addition to improving production throughput.
2. Classify and Curate Your Data
Proactively organize your data into schemas and label it through metadata to ease consumption by AI models. This step involves:
- Implementing a hierarchical tagging system
- Encoding categorical data consistently
- Including relevant metadata such as equipment IDs and batch numbers
3. Ensure Data Quality and Accuracy
AI models require high-quality, accurate data to produce reliable results. Implement data quality improvement processes, including:
- Data cleansing and validation
- Automated checks for data accuracy and completeness
- Regular data audits
The adage of “garbage in = garbage out” applies here. However, industrial AI solutions like Fero Labs have thankfully come a long way to help resolve data quality issues faster.
As long as you have the right data, and enough of it, Fero software provides AI-guided data cleaning, tagging and mapping to automate and accelerate the cleaning process, notifying the user where data looks questionable. Fero’s data capabilities are placing data preparation into the hands of engineers who are most familiar with the data and process, without needing to be trained in data science.
What happens when parts of your data is missing or incomplete? Fero Labs provides innovations around Flexible Optimization where the software can continue to adapt and optimize production even if data is missing, where traditional methods would not be able to run at all.
When making an AI software investment, ensure that data preparation and flexible optimization capabilities like these are part of the solution as this greatly accelerates your team’s agility and delivers outcomes that your team will trust.
Why should you be data-ready for a solution pilot?
Preparing data-readiness prior to engaging in a solution pilot will ensure that your project will stay on schedule, keeping all initiative team members motivated and engaged, and ensuring your project doesn’t fall into pilot purgatory. Using high quality data will enable your team to better evaluate the true value and potential of the software which will streamline your procurement process.
Pilots: Start Small and Scale
Begin with a pilot project to gain experience and refine your AI implementation processes. This approach allows you to identify and address challenges early on, setting the stage for larger-scale AI initiatives that can be explored under a broader license. This will be an indicator of your solution’s potential value over the following time period once it’s fully operational.
Advanced Data Readiness Essential Steps
These advanced data readiness steps are not requirements for the use of process optimization software. Consider these as best practices for companies wanting to make confident data-driven decisions across their entire organization.
1. Implement a Data Lineage Initiative
Data lineage traces the data’s path through various transformations and processes within data pipelines and storage systems, providing a clear visualization of how data moves and changes over time. This comprehensive mapping helps data teams understand the relationships between different data elements, ensuring data quality and reliability.
- Map data at table and column level
- Include contextual meta data at every step of the way
- Perform periodic reviews to ensure current state of data environment is reflected
2. Establish Strong Data Governance
Develop clear policies and procedures for data collection, ownership, storage, usage, and disposal. Clearly designate who is responsible for each data set across the organization, from production lines to sales teams. Assign data stewards who are responsible for managing specific data sets and ensuring data quality within their domain. Develop comprehensive data policies, and run continuous improvement audits to meet changing business needs and technical advancements.
3. Invest in Data Infrastructure
Upgrade your hardware and software to support real-time data collection and storage. This may include implementing time-series databases for continuous, time-stamped data and investing in edge computing capabilities for faster data processing.
Where most industrial AI solutions have the flexibility to run off a cloud-based system, many plants would like the flexibility to run on-prem for some or all processes. Either way, a computing upgrade investment is seldom a regretful decision.
4. Foster a Data-Driven Culture
Train your workforce on AI concepts, data literacy, and the importance of data quality. New Data Engineer roles have emerged as a key team member to ensure data readiness and quality, and to increase the overall agility of their engineering team. Encourage a data-driven mindset across the organization to ensure everyone understands their role in maintaining data readiness. This includes using your industrial AI solution as a single source of truth for your entire operational workflows.
Invest in Your Data Readiness Ad Nauseam
Becoming data-ready is an ongoing process that requires continuous effort and investment. However, the potential benefits—including increased efficiency, reduced costs, greater agility, faster decision-making, and improved sustainability—make it a worthwhile endeavor for any forward-thinking industrial organization.
Preparing your data infrastructure for AI-driven process optimization will enable you to get the most value from your software pilot program. Following the steps for basic data readiness will put you ahead of many manufacturing businesses. Following the advanced data readiness steps will make it part of your company DNA which will be a clear competitive differentiator, providing speed to market agility and bottomline efficiencies.
As you embark on your journey towards data readiness, keep in mind that the goal is not just to collect data, but to transform it into actionable insights that drive business value. With proper preparation and a commitment to data quality, your organization can harness the full potential of industrial AI to optimize operations and stay competitive in an increasingly data-driven world.
Learn how Fero Labs can accelerate your data readiness and extract value from your collected data.
